Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.

Digital Transformation of Signatures: Suggesting Functional Symmetry Approach for Loan Agreements

This article aims to formulate proposals for regulatory bodies whose implementation would ensure the effective introduction of civil circulation into electronic signatures, with minimal costs for economic entities. While electronic signatures have been widely discussed in academic literature, there are still gaps in the understanding of similarities and differences between electronic and handwritten signatures, the functional specifics of the relationship between them, and the role of electronic signatures for electronic contract. Our research has allowed us to overcome this gap adopting a functional symmetry approach based on measuring the distance between fuzzy sets and the Mamdani fuzzy inference algorithm. This made it possible to form an estimate of the degree of functional symmetry between different types of signatures in a fuzzy and exact form. Correspondingly, we argue that the signature can be viewed as a set of procedures rather than as a single act in order to achieve functional symmetry with a handwritten signature. The case of online lending was used to test and prove this hypothesis. Therefore, regulating electronic signatures needs to focus on the efficiency of this processes for ex ante identification, capturing the intent, ensuring the inalterability and providing reliable evidence, irrespective of the type of electronic signature that is used. It was also revealed that the proposed functional symmetry approach can be combined with a fuzziness index analysis to provide new prospects for further research.

Offline Signature Identification and Verification Based on Capsule Representations

Offline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.